Python cv2.ORB Examples
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code examples of cv2.ORB().
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Example #1
Source File: findobj.py From airtest with BSD 3-Clause "New" or "Revised" License | 6 votes |
def init_feature(name): chunks = name.split('-') if chunks[0] == 'sift': detector = cv2.SIFT() norm = cv2.NORM_L2 elif chunks[0] == 'surf': detector = cv2.SURF(800) norm = cv2.NORM_L2 elif chunks[0] == 'orb': detector = cv2.ORB(400) norm = cv2.NORM_HAMMING else: return None, None if 'flann' in chunks: if norm == cv2.NORM_L2: flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) else: flann_params= dict(algorithm = FLANN_INDEX_LSH, table_number = 6, # 12 key_size = 12, # 20 multi_probe_level = 1) #2 matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) else: matcher = cv2.BFMatcher(norm) return detector, matcher
Example #2
Source File: find_obj.py From airtest with BSD 3-Clause "New" or "Revised" License | 6 votes |
def init_feature(name): chunks = name.split('-') if chunks[0] == 'sift': detector = cv2.SIFT() norm = cv2.NORM_L2 elif chunks[0] == 'surf': detector = cv2.SURF(800) norm = cv2.NORM_L2 elif chunks[0] == 'orb': detector = cv2.ORB(400) norm = cv2.NORM_HAMMING else: return None, None if 'flann' in chunks: if norm == cv2.NORM_L2: flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) else: flann_params= dict(algorithm = FLANN_INDEX_LSH, table_number = 6, # 12 key_size = 12, # 20 multi_probe_level = 1) #2 matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) else: matcher = cv2.BFMatcher(norm) return detector, matcher
Example #3
Source File: find_obj.py From ImageAnalysis with MIT License | 6 votes |
def init_feature(name): chunks = name.split('-') if chunks[0] == 'sift': detector = cv2.SIFT() norm = cv2.NORM_L2 elif chunks[0] == 'surf': detector = cv2.SURF(400) norm = cv2.NORM_L2 elif chunks[0] == 'orb': detector = cv2.ORB(400) norm = cv2.NORM_HAMMING else: return None, None if 'flann' in chunks: if norm == cv2.NORM_L2: flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) else: flann_params= dict(algorithm = FLANN_INDEX_LSH, table_number = 6, # 12 key_size = 12, # 20 multi_probe_level = 1) #2 matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) else: matcher = cv2.BFMatcher(norm) return detector, matcher
Example #4
Source File: find_obj.py From PyCV-time with MIT License | 6 votes |
def init_feature(name): chunks = name.split('-') if chunks[0] == 'sift': detector = cv2.SIFT() norm = cv2.NORM_L2 elif chunks[0] == 'surf': detector = cv2.SURF(800) norm = cv2.NORM_L2 elif chunks[0] == 'orb': detector = cv2.ORB(400) norm = cv2.NORM_HAMMING else: return None, None if 'flann' in chunks: if norm == cv2.NORM_L2: flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) else: flann_params= dict(algorithm = FLANN_INDEX_LSH, table_number = 6, # 12 key_size = 12, # 20 multi_probe_level = 1) #2 matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) else: matcher = cv2.BFMatcher(norm) return detector, matcher
Example #5
Source File: ftlib.py From sea_ice_drift with GNU General Public License v3.0 | 5 votes |
def find_key_points(image, edgeThreshold=34, nFeatures=100000, nLevels=7, patchSize=34, **kwargs): ''' Initiate detector and find key points on an image Parameters ---------- image : 2D UInt8 Numpy array - image edgeThreshold : int - parameter for OpenCV detector nFeatures : int - parameter for OpenCV detector nLevels : int - parameter for OpenCV detector patchSize : int - parameter for OpenCV detector Returns ------- keyPoints : list - coordinates of keypoint on image descriptors : list - binary descriptos of kepoints ''' if cv2.__version__.startswith('3.') or cv2.__version__.startswith('4.'): detector = cv2.ORB_create() detector.setEdgeThreshold(edgeThreshold) detector.setMaxFeatures(nFeatures) detector.setNLevels(nLevels) detector.setPatchSize(patchSize) else: detector = cv2.ORB() detector.setInt('edgeThreshold', edgeThreshold) detector.setInt('nFeatures', nFeatures) detector.setInt('nLevels', nLevels) detector.setInt('patchSize', patchSize) print('ORB detector initiated') keyPoints, descriptors = detector.detectAndCompute(image, None) print('Key points found: %d' % len(keyPoints)) return keyPoints, descriptors
Example #6
Source File: plane_tracker.py From PyCV-time with MIT License | 5 votes |
def __init__(self): self.detector = cv2.ORB( nfeatures = 1000 ) self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) self.targets = []
Example #7
Source File: plane_tracker.py From PyCV-time with MIT License | 5 votes |
def __init__(self): self.detector = cv2.ORB( nfeatures = 1000 ) self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) self.targets = []
Example #8
Source File: find_obj.py From PyCV-time with MIT License | 5 votes |
def init_feature(name): chunks = name.split('-') if chunks[0] == 'sift': detector = cv2.xfeatures2d.SIFT() norm = cv2.NORM_L2 elif chunks[0] == 'surf': detector = cv2.xfeatures2d.SURF(800) norm = cv2.NORM_L2 elif chunks[0] == 'orb': detector = cv2.ORB(400) norm = cv2.NORM_HAMMING elif chunks[0] == 'akaze': detector = cv2.AKAZE() norm = cv2.NORM_HAMMING elif chunks[0] == 'brisk': detector = cv2.BRISK() norm = cv2.NORM_HAMMING else: return None, None if 'flann' in chunks: if norm == cv2.NORM_L2: flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) else: flann_params= dict(algorithm = FLANN_INDEX_LSH, table_number = 6, # 12 key_size = 12, # 20 multi_probe_level = 1) #2 matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) else: matcher = cv2.BFMatcher(norm) return detector, matcher
Example #9
Source File: servoing_designed_features_quad_panda3d_env.py From citysim3d with MIT License | 4 votes |
def __init__(self, action_space, feature_type=None, filter_features=None, max_time_steps=100, distance_threshold=4.0, **kwargs): """ filter_features indicates whether to filter out key points that are not on the object in the current image. Key points in the target image are always filtered out. """ SimpleQuadPanda3dEnv.__init__(self, action_space, **kwargs) ServoingEnv.__init__(self, env=self, max_time_steps=max_time_steps, distance_threshold=distance_threshold) lens = self.camera_node.node().getLens() self._observation_space.spaces['points'] = BoxSpace(np.array([-np.inf, lens.getNear(), -np.inf]), np.array([np.inf, lens.getFar(), np.inf])) film_size = tuple(int(s) for s in lens.getFilmSize()) self.mask_camera_sensor = Panda3dMaskCameraSensor(self.app, (self.skybox_node, self.city_node), size=film_size, near_far=(lens.getNear(), lens.getFar()), hfov=lens.getFov()) for cam in self.mask_camera_sensor.cam: cam.reparentTo(self.camera_sensor.cam) self.filter_features = True if filter_features is None else False self._feature_type = feature_type or 'sift' if cv2.__version__.split('.')[0] == '3': from cv2.xfeatures2d import SIFT_create, SURF_create from cv2 import ORB_create if self.feature_type == 'orb': # https://github.com/opencv/opencv/issues/6081 cv2.ocl.setUseOpenCL(False) else: SIFT_create = cv2.SIFT SURF_create = cv2.SURF ORB_create = cv2.ORB if self.feature_type == 'sift': self._feature_extractor = SIFT_create() elif self.feature_type == 'surf': self._feature_extractor = SURF_create() elif self.feature_type == 'orb': self._feature_extractor = ORB_create() else: raise ValueError("Unknown feature extractor %s" % self.feature_type) if self.feature_type == 'orb': self._matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) else: self._matcher = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True) self._target_key_points = None self._target_descriptors = None